In hydrocarbon exploration and production, successful delivery of hydrocarbon wells is often limited by the inability to accurately describe the in-situ wellbore environment in an appropriate time frame. This generally stems from either a lack of downhole data or an inability to process the data gathered into meaningful information. The fact that measurements are commonly made at only two points in the well (at the surface and in the bottom hole assembly (BHA)) also imposes limitations on the ability to understand what is happening downhole. Due to acquiring measurements at only two points, the properties of a fraction of the wellbore being drilled—in general just the area around the BHA—are obtained, leaving significant gaps in assessing the condition of the wellbore. This can affect the ability to accurately detect and diagnose problems (both cause and location).
This lack of empirical data during drilling means that other techniques must be employed in an attempt to fill in the blanks. This usually involves the use of either first principle, statistical or hybrid models. While these models can be useful in certain situations, it is desirable to actually “see” what is happening throughout the wellbore, irrespective of the quality of supporting models (or their setup). Accordingly, there is a need for methods and systems of determining borehole conditions using distributed measurement data along the drill string.